Models of vegetation composition based on climate predictors
Based on code from Martin Holdrege’s Sagebrush-Fire paper
Alice Stears
2025-05-20
- Dependencies
- read in data
- Prep data
- Add a constant to the response variable (+2) so that models run…
- Identify the ecoregion and response variable type to use in this model run
- Currently, subsampling data from the “Texas Coastal Plain”, since it’s quite different from other regions and is really messing with model fit
- Visualize the predictor variables
- Scale the predictor variables for the model-fitting process
- Subset the data to only include data for the ecoregion of interest
- Visualize the response variable
- Plot predictor vars against each other
- Predictor variables compared to binned response variables
- Model Fitting
- Visualizations of Model Predictions and Residuals – using best lambda model
- Cross-validation
- Using best lambda model
- Use terms from global model to re-fit and predict on different held out regions
- Figures show residuals for each of the models fit to held-out ecoregions
- Below are the RMSE and bias values for predictions made for each holdout level II ecoregion, compared to predictions from the global model for that same ecoregion
- Using second
best lambda model (either 1se or 1/2se)
- Use terms from global model to re-fit and predict on different held out regions
- Figures show residuals for each of the models fit to held-out ecoregions
- Below are the RMSE and bias values for predictions made for each holdout level II ecoregion, compared to predictions from the global model for that same ecoregion
- Using best lambda model
- Save output
- session info
The data consists of vegetation % cover by functional group from across CONUS (from AIM, FIA, LANDFIRE, and RAP), as well as climate variables from DayMet, which have been aggregated into mean interannual conditions accross multiple temporal windows.
Dependencies
User defined parameters
print(params)## $autoKfold
## [1] FALSE
##
## $run
## [1] FALSE
##
## $test_run
## [1] FALSE
##
## $save_figs
## [1] FALSE
##
## $trimAnomalies
## [1] FALSE
##
## $ecoregion
## [1] "shrubGrass"
##
## $response
## [1] "BareGroundCover"
##
## $removeTexasLouisianaPlain
## [1] FALSE# set to true if want to run for a limited number of rows (i.e. for code testing)
test_run <- params$test_run
save_figs <- params$save_figs
response <- params$response
fit_sample <- TRUE # fit model to a sample of the data
n_train <- 5e4 # sample size of the training data
n_test <- 1e6 # sample size of the testing data (if this is too big the decile dotplot code throws memory errors)
trimAnom <- params$trimAnomalies
removeTLP <- params$removeTexasLouisianaPlain
run <- params$run
autoKfold <- params$autoKfold# set option so resampled dataset created here reproduces earlier runs of this code with dplyr 1.0.10
source("../../../Functions/glmTransformsIterates.R")
source("../../../Functions/transformPreds.R")
source("../../../Functions/StepBeta_mine.R")
#source("src/fig_params.R")
#source("src/modeling_functions.R")
library(ggspatial)
library(terra)
library(tidyterra)
library(sf)
library(caret)
library(tidyverse)
library(GGally) # for ggpairs()
library(pdp) # for partial dependence plots
library(gridExtra)
library(knitr)
library(patchwork) # for figure insets etc.
library(ggtext)
library(StepBeta)
theme_set(theme_classic())
library(here)
library(rsample)
library(kableExtra)
library(glmnet)
library(USA.state.boundaries)read in data
Data compiled in the prepDataForModels.R script
here::i_am("Analysis/VegComposition/ModelFitting/02_ModelFitting.Rmd")
modDat <- readRDS( here("Data_processed", "CoverData", "DataForModels_spatiallyAveraged_withSoils_noSf.rds"))
## there are some values of the annual wet degree days 5th percentile that have -Inf?? change to lowest value for now?
modDat[is.infinite(modDat$annWetDegDays_5percentile_3yrAnom), "annWetDegDays_5percentile_3yrAnom"] <- -47.8
## same, but for annual water deficit 95th percentile
modDat[is.infinite(modDat$annWaterDeficit_95percentile_3yrAnom), "annWaterDeficit_95percentile_3yrAnom"] <- -600
# # Convert total cover variables into proportions (for later use in beta regression models) ... proportions are already scaled from zero to 1
# modDat <- modDat %>%
# mutate(TotalTreeCover = TotalTreeCover/100,
# CAMCover = CAMCover/100,
# TotalHerbaceousCover = TotalHerbaceousCover/100,
# BareGroundCover = BareGroundCover/100,
# ShrubCover = ShrubCover/100
# )
# For all response variables, make sure there are no 0s add or subtract .0001 from each, since the Gamma model framework can't handle that
modDat[modDat$TotalTreeCover == 0 & !is.na(modDat$TotalTreeCover), "TotalTreeCover"] <- 0.0001
modDat[modDat$CAMCover == 0 & !is.na(modDat$CAMCover), "CAMCover"] <- 0.0001
modDat[modDat$TotalHerbaceousCover == 0 & !is.na(modDat$TotalHerbaceousCover), "TotalHerbaceousCover"] <- 0.0001
modDat[modDat$BareGroundCover == 0 & !is.na(modDat$BareGroundCover), "BareGroundCover"] <- 0.0001
modDat[modDat$ShrubCover == 0 & !is.na(modDat$ShrubCover), "ShrubCover"] <- 0.0001
modDat[modDat$BroadleavedTreeCover_prop == 0 & !is.na(modDat$BroadleavedTreeCover_prop), "BroadleavedTreeCover_prop"] <- 0.0001
modDat[modDat$NeedleLeavedTreeCover_prop == 0 & !is.na(modDat$NeedleLeavedTreeCover_prop), "NeedleLeavedTreeCover_prop"] <- 0.0001
modDat[modDat$C4Cover_prop == 0 & !is.na(modDat$C4Cover_prop), "C4Cover_prop"] <- 0.0001
modDat[modDat$C3Cover_prop == 0 & !is.na(modDat$C3Cover_prop), "C3Cover_prop"] <- 0.0001
modDat[modDat$ForbCover_prop == 0 & !is.na(modDat$ForbCover_prop), "ForbCover_prop"] <- 0.0001
#
# modDat[modDat$TotalTreeCover ==1& !is.na(modDat$TotalTreeCover), "TotalTreeCover"] <- 0.999
# modDat[modDat$CAMCover ==1& !is.na(modDat$CAMCover), "CAMCover"] <- 0.999
# modDat[modDat$TotalHerbaceousCover ==1 & !is.na(modDat$TotalHerbaceousCover), "TotalHerbaceousCover"] <- 0.999
# modDat[modDat$BareGroundCover ==1& !is.na(modDat$BareGroundCover), "BareGroundCover"] <- 0.999
# modDat[modDat$ShrubCover ==1& !is.na(modDat$ShrubCover), "ShrubCover"] <- 0.999
# modDat[modDat$BroadleavedTreeCover_prop ==1& !is.na(modDat$BroadleavedTreeCover_prop), "BroadleavedTreeCover_prop"] <- 0.999
# modDat[modDat$NeedleLeavedTreeCover_prop ==1& !is.na(modDat$NeedleLeavedTreeCover_prop), "NeedleLeavedTreeCover_prop"] <- 0.999
# modDat[modDat$C4Cover_prop ==1& !is.na(modDat$C4Cover_prop), "C4Cover_prop"] <- 0.999
# modDat[modDat$C3Cover_prop ==1& !is.na(modDat$C3Cover_prop), "C3Cover_prop"] <- 0.999
# modDat[modDat$ForbCover_prop ==1& !is.na(modDat$ForbCover_prop), "ForbCover_prop"] <- 0.999Prep data
set.seed(1234)
modDat_1 <- modDat %>%
dplyr::select(-c(prcp_annTotal:annVPD_min)) %>%
# mutate(Lon = st_coordinates(.)[,1],
# Lat = st_coordinates(.)[,2]) %>%
# st_drop_geometry() %>%
# filter(!is.na(newRegion))
rename("tmin" = tmin_meanAnnAvg_CLIM,
"tmax" = tmax_meanAnnAvg_CLIM, #1
"tmean" = tmean_meanAnnAvg_CLIM,
"prcp" = prcp_meanAnnTotal_CLIM,
"t_warm" = T_warmestMonth_meanAnnAvg_CLIM,
"t_cold" = T_coldestMonth_meanAnnAvg_CLIM,
"prcp_wet" = precip_wettestMonth_meanAnnAvg_CLIM,
"prcp_dry" = precip_driestMonth_meanAnnAvg_CLIM,
"prcp_seasonality" = precip_Seasonality_meanAnnAvg_CLIM, #2
"prcpTempCorr" = PrecipTempCorr_meanAnnAvg_CLIM, #3
"abvFreezingMonth" = aboveFreezing_month_meanAnnAvg_CLIM,
"isothermality" = isothermality_meanAnnAvg_CLIM, #4
"annWatDef" = annWaterDeficit_meanAnnAvg_CLIM,
"annWetDegDays" = annWetDegDays_meanAnnAvg_CLIM,
"VPD_mean" = annVPD_mean_meanAnnAvg_CLIM,
"VPD_max" = annVPD_max_meanAnnAvg_CLIM, #5
"VPD_min" = annVPD_min_meanAnnAvg_CLIM, #6
"VPD_max_95" = annVPD_max_95percentile_CLIM,
"annWatDef_95" = annWaterDeficit_95percentile_CLIM,
"annWetDegDays_5" = annWetDegDays_5percentile_CLIM,
"frostFreeDays_5" = durationFrostFreeDays_5percentile_CLIM,
"frostFreeDays" = durationFrostFreeDays_meanAnnAvg_CLIM,
"soilDepth" = soilDepth, #7
"clay" = surfaceClay_perc,
"sand" = avgSandPerc_acrossDepth, #8
"coarse" = avgCoarsePerc_acrossDepth, #9
"carbon" = avgOrganicCarbonPerc_0_3cm, #10
"AWHC" = totalAvailableWaterHoldingCapacity,
## anomaly variables
tmean_anom = tmean_meanAnnAvg_3yrAnom, #15
tmin_anom = tmin_meanAnnAvg_3yrAnom, #16
tmax_anom = tmax_meanAnnAvg_3yrAnom, #17
prcp_anom = prcp_meanAnnTotal_3yrAnom, #18
t_warm_anom = T_warmestMonth_meanAnnAvg_3yrAnom, #19
t_cold_anom = T_coldestMonth_meanAnnAvg_3yrAnom, #20
prcp_wet_anom = precip_wettestMonth_meanAnnAvg_3yrAnom, #21
precp_dry_anom = precip_driestMonth_meanAnnAvg_3yrAnom, #22
prcp_seasonality_anom = precip_Seasonality_meanAnnAvg_3yrAnom, #23
prcpTempCorr_anom = PrecipTempCorr_meanAnnAvg_3yrAnom, #24
aboveFreezingMonth_anom = aboveFreezing_month_meanAnnAvg_3yrAnom, #25
isothermality_anom = isothermality_meanAnnAvg_3yrAnom, #26
annWatDef_anom = annWaterDeficit_meanAnnAvg_3yrAnom, #27
annWetDegDays_anom = annWetDegDays_meanAnnAvg_3yrAnom, #28
VPD_mean_anom = annVPD_mean_meanAnnAvg_3yrAnom, #29
VPD_min_anom = annVPD_min_meanAnnAvg_3yrAnom, #30
VPD_max_anom = annVPD_max_meanAnnAvg_3yrAnom, #31
VPD_max_95_anom = annVPD_max_95percentile_3yrAnom, #32
annWatDef_95_anom = annWaterDeficit_95percentile_3yrAnom, #33
annWetDegDays_5_anom = annWetDegDays_5percentile_3yrAnom , #34
frostFreeDays_5_anom = durationFrostFreeDays_5percentile_3yrAnom, #35
frostFreeDays_anom = durationFrostFreeDays_meanAnnAvg_3yrAnom #36
) %>%
select(-c(tmin_meanAnnAvg_3yr:durationFrostFreeDays_meanAnnAvg_3yr))
# small dataset for if testing the data
if(test_run) {
modDat_1 <- slice_sample(modDat_1, n = 1e5)
}Add a constant to the response variable (+2) so that models run…
modDat_1 <- modDat_1 %>%
mutate(response_transformed = .[[response]] + 2)
names(modDat_1)[names(modDat_1) == "response_transformed"] <- paste0(response, "_transformed")Identify the ecoregion and response variable type to use in this model run
ecoregion <- params$ecoregion
response <- params$response
print(paste0("In this model run, the ecoregion is ", ecoregion," and the response variable is ",response))## [1] "In this model run, the ecoregion is shrubGrass and the response variable is BareGroundCover"Currently, subsampling data from the “Texas Coastal Plain”, since it’s quite different from other regions and is really messing with model fit
if (removeTLP == TRUE) {
modDat_1 <- modDat_1 %>%
filter(NA_L2NAME != "TEXAS-LOUISIANA COASTAL PLAIN")
} else {
modDat_1_noLA <- modDat_1 %>%
filter(NA_L2NAME != "TEXAS-LOUISIANA COASTAL PLAIN")
modDat_1_LA <- modDat_1 %>%
filter(NA_L2NAME == "TEXAS-LOUISIANA COASTAL PLAIN")
# sample points
modDat_1 <- modDat_1_LA %>%
slice_sample(n = round(nrow(modDat_1_LA)*.3)) %>%
rbind(modDat_1_noLA)
}Visualize the predictor variables
The following are the candidate predictor variables for this ecoregion:
if (ecoregion == "shrubGrass") {
# select potential predictor variables for the ecoregion of interest
prednames <-
c(
"tmean" , "prcp" ,"prcp_seasonality" ,"prcpTempCorr" ,
"isothermality" , "annWatDef" ,"sand" ,"coarse" ,
"carbon" , "AWHC" ,"tmin_anom" ,"tmax_anom" ,
"t_warm_anom" , "prcp_wet_anom" ,"precp_dry_anom" ,"prcp_seasonality_anom" ,
"prcpTempCorr_anom" , "aboveFreezingMonth_anom" ,"isothermality_anom" ,"annWatDef_anom" ,
"annWetDegDays_anom", "VPD_mean_anom" ,"VPD_min_anom" ,"frostFreeDays_5_anom" )
} else if (ecoregion %in% c("forest", "eastForest", "westForest")) {
# select potential predictor variables for the ecoregion of interest
prednames <-
c(
"tmean" ,"prcp" , "prcp_dry" , "prcpTempCorr" ,
"isothermality" ,"annWatDef" , "clay" , "sand" ,
"coarse" ,"carbon" , "AWHC" , "tmin_anom" ,
"tmax_anom" ,"prcp_anom" , "prcp_wet_anom" , "precp_dry_anom" ,
"prcp_seasonality_anom" ,"prcpTempCorr_anom" , "aboveFreezingMonth_anom" , "isothermality_anom",
"annWatDef_anom" ,"annWetDegDays_anom" , "VPD_mean_anom" , "VPD_max_95_anom" ,
"frostFreeDays_5_anom" )
} else if (ecoregion == "CONUS") {
prednames <- c(
"tmean" ,"prcp" ,"prcp_seasonality", "prcpTempCorr" , "isothermality" ,
"annWetDegDays" ,"sand" ,"coarse" , "AWHC" , "tmin_anom" ,
"tmax_anom" ,"prcp_wet_anom" ,"precp_dry_anom" , "prcp_seasonality_anom", "prcpTempCorr_anom" ,
"aboveFreezingMonth_anom" ,"isothermality_anom" ,"annWatDef_anom" , "annWetDegDays_anom" , "VPD_mean_anom" ,
"VPD_max_95_anom" ,"frostFreeDays_5_anom"
)
}
# get the name of the transformed response
response_t <- paste0(response, "_transformed")Scale the predictor variables for the model-fitting process
allPreds <- modDat_1 %>%
select(tmin:frostFreeDays,tmean_anom:frostFreeDays_anom, soilDepth:AWHC) %>%
names()
modDat_1_s <- modDat_1 %>%
mutate(across(all_of(allPreds), base::scale, .names = "{.col}_s"))
# names(modDat_1_s) <- c(names(modDat_1),
# paste0(prednames, "_s")
# )
#save model input data after its been scaled
saveRDS(modDat_1_s, file = "./models/scaledModelInputData.rds")Subset the data to only include data for the ecoregion of interest
if (ecoregion == "shrubGrass") {
# select data for the ecoregion of interest
modDat_1_s <- modDat_1_s %>%
filter(newRegion == "dryShrubGrass")
} else if (ecoregion == "forest") {
# select data for the ecoregion of interest
modDat_1_s <- modDat_1_s %>%
filter(newRegion %in% c("eastForest", "westForest"))
} else if (ecoregion == "CONUS") {
modDat_1_s <- modDat_1_s
} else if (ecoregion == "eastForest") {
modDat_1_s <- modDat_1_s %>%
filter(newRegion == "eastForest")
} else if (ecoregion == "westForest") {
modDat_1_s <- modDat_1_s %>%
filter(newRegion == "westForest")
}
# remove the rows that have no observations for the response variable of interest
modDat_1_s <- modDat_1_s[!is.na(modDat_1_s[,response]),]# subset the data to only include these predictors, and remove any remaining NAs
modDat_1_s <- modDat_1_s %>%
dplyr::select(prednames, paste0(prednames, "_s"), response, response_t, newRegion, Year, Long, Lat, NA_L1NAME, NA_L2NAME) %>%
drop_na()
names(prednames) <- prednames
df_pred <- modDat_1_s[, prednames]
#
# # print the list of predictor variables
# knitr::kable(format = "html", data.frame("Possible_Predictors" = prednames), row.names = FALSE
# ) %>%
# kable_styling(bootstrap_options = c("striped", "hover", "condensed"))Visualize the response variable
hist(modDat_1_s[,response], main = paste0("Histogram of ",response),
xlab = paste0(response))
create_summary <- function(df) {
df %>%
pivot_longer(cols = everything(),
names_to = 'variable') %>%
group_by(variable) %>%
summarise(across(value, .fns = list(mean = ~mean(.x, na.rm = TRUE), min = ~min(.x, na.rm = TRUE),
median = ~median(.x, na.rm = TRUE), max = ~max(.x, na.rm = TRUE)))) %>%
mutate(across(where(is.numeric), round, 4))
}
modDat_1_s[prednames] %>%
create_summary() %>%
knitr::kable(caption = 'summaries of possible predictor variables') %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) | variable | value_mean | value_min | value_median | value_max |
|---|---|---|---|---|
| AWHC | 14.1732 | 0.6319 | 13.7195 | 35.1058 |
| VPD_mean_anom | -0.0213 | -0.3320 | -0.0198 | 0.2301 |
| VPD_min_anom | -0.0128 | -0.1764 | -0.0157 | 0.1399 |
| aboveFreezingMonth_anom | 0.0616 | -3.0952 | 0.0333 | 3.4762 |
| annWatDef | 119.5108 | 1.5871 | 96.3778 | 548.3523 |
| annWatDef_anom | -0.0576 | -3.9347 | -0.0534 | 1.0000 |
| annWetDegDays_anom | 0.0094 | -2.1708 | 0.0083 | 1.0000 |
| carbon | 0.9807 | 0.0001 | 0.7942 | 27.0162 |
| coarse | 9.3463 | 0.0000 | 6.2019 | 79.9649 |
| frostFreeDays_5_anom | -17.2170 | -246.0000 | -6.0000 | 55.8000 |
| isothermality | 37.9477 | 23.2374 | 37.7983 | 63.3057 |
| isothermality_anom | 0.5067 | -15.6968 | 0.4437 | 14.0338 |
| prcp | 400.4510 | 49.2000 | 362.1239 | 1701.0497 |
| prcpTempCorr | 0.0089 | -0.7841 | 0.0493 | 0.6890 |
| prcpTempCorr_anom | 0.0122 | -0.6495 | 0.0165 | 0.5744 |
| prcp_seasonality | 0.9878 | 0.5255 | 0.9412 | 2.2148 |
| prcp_seasonality_anom | -0.0126 | -0.8313 | -0.0051 | 0.4743 |
| prcp_wet_anom | -0.0049 | -1.9576 | 0.0127 | 0.7548 |
| precp_dry_anom | -0.0497 | -9.0000 | 0.1035 | 1.0000 |
| sand | 47.7406 | 2.5763 | 46.9536 | 98.9991 |
| t_warm_anom | -0.4493 | -6.3974 | -0.4604 | 3.3598 |
| tmax_anom | -0.2724 | -3.4286 | -0.3035 | 2.3198 |
| tmean | 11.1485 | -2.2935 | 9.8180 | 24.7600 |
| tmin_anom | -0.4643 | -4.1767 | -0.4429 | 4.7443 |
# response_summary <- modDat_1_s %>%
# dplyr::select(#where(is.numeric), -all_of(pred_vars),
# matches(response)) %>%
# create_summary()
#
#
# kable(response_summary,
# caption = 'summaries of response variables, calculated using paint') %>%
# kable_styling(bootstrap_options = c("striped", "hover", "condensed")) Histograms of raw and scaled predictors
scaleFigDat_1 <- modDat_1_s %>%
dplyr::select(c(Long, Lat, Year, prednames)) %>%
pivot_longer(cols = all_of(names(prednames)),
names_to = "predNames",
values_to = "predValues_unScaled")
scaleFigDat_2 <- modDat_1_s %>%
dplyr::select(c(Long, Lat, Year, paste0(prednames, "_s"))) %>%
pivot_longer(cols = all_of(paste0(prednames,"_s"
)),
names_to = "predNames",
values_to = "predValues_scaled",
names_sep = ) %>%
mutate(predNames = str_replace(predNames, pattern = "_s$", replacement = ""))
scaleFigDat_3 <- scaleFigDat_1 %>%
left_join(scaleFigDat_2)
ggplot(scaleFigDat_3) +
facet_wrap(~predNames, scales = "free") +
geom_histogram(aes(predValues_unScaled), fill = "lightgrey", col = "darkgrey") +
geom_histogram(aes(predValues_scaled), fill = "lightblue", col = "blue") +
xlab ("predictor variable values") +
ggtitle("Comparing the distribution of unscaled (grey) to scaled (blue) predictor variables")
modDat_1_s <- modDat_1_s %>%
rename_with(~paste0(.x, "_raw"),
any_of(names(prednames))) %>%
rename_with(~str_remove(.x, "_s$"),
any_of(paste0(names(prednames), "_s")))Plot predictor vars against each other
set.seed(12011993)
# function for colors
my_fn <- function(data, mapping, method="p", use="pairwise", ...){
# grab data
x <- eval_data_col(data, mapping$x)
y <- eval_data_col(data, mapping$y)
# calculate correlation
corr <- cor(x, y, method=method, use=use)
# calculate colour based on correlation value
# Here I have set a correlation of minus one to blue,
# zero to white, and one to red
# Change this to suit: possibly extend to add as an argument of `my_fn`
colFn <- colorRampPalette(c("red", "white", "blue"), interpolate ='spline')
fill <- colFn(100)[findInterval(corr, seq(-1, 1, length=100))]
ggally_cor(data = data, mapping = mapping, size = 2.5, stars = FALSE,
digits = 2, colour = I("black"),...) +
theme_void() +
theme(panel.background = element_rect(fill=fill))
}
if (run == TRUE) {
(corrPlot <- modDat_1_s %>%
dplyr::select(prednames) %>%
slice_sample(n = 5e4) %>%
#select(-matches("_")) %>%
ggpairs( upper = list(continuous = my_fn, size = .1), lower = list(continuous = GGally::wrap("points", alpha = 0.1, size=0.1)), progress = FALSE))
base::saveRDS(corrPlot, paste0("../ModelFitting/models/", response, "_",ecoregion, "_corrPlot.rds"))
} else {
# corrPlot <- readRDS(paste0("../ModelFitting/models/", response, "_",ecoregion, "_corrPlot.rds"))
# (corrPlot)
print(c("See previous correlation figures"))
}## [1] "See previous correlation figures"Predictor variables compared to binned response variables
set.seed(12011993)
# vector of name of response variables
vars_response <- response
# longformat dataframes for making boxplots
df_sample_plots <- modDat_1_s %>%
slice_sample(n = 5e4) %>%
rename(response = all_of(response_t)) %>%
mutate(response = case_when(
response <= .25 ~ ".25",
response > .25 & response <=.5 ~ ".5",
response > .5 & response <=.75 ~ ".75",
response >= .75 ~ "1",
)) %>%
dplyr::select(c(response, prednames)) %>%
tidyr::pivot_longer(cols = unname(prednames),
names_to = "predictor",
values_to = "value"
)
ggplot(df_sample_plots, aes_string(x= "response", y = 'value')) +
geom_boxplot() +
facet_wrap(~predictor , scales = 'free_y') +
ylab("Predictor Variable Values") +
xlab(response)Model Fitting
Visualize the spatial blocks and how they differ across environmental space
First, if there are observations in Louisiana, sub-sample them so they’re not so over-represented in the dataset
## make data into spatial format
modDat_1_sf <- modDat_1_s %>%
st_as_sf(coords = c("Long", "Lat"), crs = st_crs("PROJCRS[\"unnamed\",\n BASEGEOGCRS[\"unknown\",\n DATUM[\"unknown\",\n ELLIPSOID[\"Spheroid\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1,\n ID[\"EPSG\",9001]]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433,\n ID[\"EPSG\",9122]]]],\n CONVERSION[\"Lambert Conic Conformal (2SP)\",\n METHOD[\"Lambert Conic Conformal (2SP)\",\n ID[\"EPSG\",9802]],\n PARAMETER[\"Latitude of false origin\",42.5,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8821]],\n PARAMETER[\"Longitude of false origin\",-100,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8822]],\n PARAMETER[\"Latitude of 1st standard parallel\",25,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8823]],\n PARAMETER[\"Latitude of 2nd standard parallel\",60,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8824]],\n PARAMETER[\"Easting at false origin\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8826]],\n PARAMETER[\"Northing at false origin\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8827]]],\n CS[Cartesian,2],\n AXIS[\"easting\",east,\n ORDER[1],\n LENGTHUNIT[\"metre\",1,\n ID[\"EPSG\",9001]]],\n AXIS[\"northing\",north,\n ORDER[2],\n LENGTHUNIT[\"metre\",1,\n ID[\"EPSG\",9001]]]]"))
# download map info for visualization
data(state_boundaries_wgs84)
cropped_states <- suppressMessages(state_boundaries_wgs84 %>%
dplyr::filter(NAME!="Hawaii") %>%
dplyr::filter(NAME!="Alaska") %>%
dplyr::filter(NAME!="Puerto Rico") %>%
dplyr::filter(NAME!="American Samoa") %>%
dplyr::filter(NAME!="Guam") %>%
dplyr::filter(NAME!="Commonwealth of the Northern Mariana Islands") %>%
dplyr::filter(NAME!="United States Virgin Islands") %>%
sf::st_sf() %>%
sf::st_transform(sf::st_crs(modDat_1_sf))) #%>%
#sf::st_crop(sf::st_bbox(modDat_1_sf)+c(-1,-1,1,1))
if (ecoregion %in% c("Forest", "eastForest", "forest")){
modDat_1_s$uniqueID <- 1:nrow(modDat_1_s)
modDat_1_sf$uniqueID <- 1:nrow(modDat_1_sf)
# find which observations overlap with Louisiana
obs_LA_temp <- st_intersects(modDat_1_sf, cropped_states[cropped_states$NAME == "Louisiana",], sparse = FALSE)
if (sum(obs_LA_temp) > 0) {
obs_LA_1 <- modDat_1_sf[which(obs_LA_temp == TRUE, arr.ind = TRUE)[,1],]
# now, find only those within the oversampled area (near the coast)
dims <- c(xmin = 730439.1, xmax = 1042195.5, ymax = -1222745.2, ymin = -1390430.9)
badBox <- st_bbox(dims) %>%
st_as_sfc() %>%
st_set_crs(value = st_crs(modDat_1_sf))
obs_LA_temp2 <- st_intersects(obs_LA_1, badBox, sparse = FALSE)
obs_LA_2 <- obs_LA_1[which(obs_LA_temp2 == TRUE, arr.ind = TRUE)[,1],]
# subsample so there aren't so many
# get every 7th observation
obs_LA_sampled <- obs_LA_2[seq(from = 1, to = nrow(obs_LA_2), by = 10),]
# remove observations that aren't sampled from the larger dataset
obsToRemove <- obs_LA_2[!(obs_LA_2$uniqueID %in% obs_LA_sampled$uniqueID),]
modDat_1_sf <- modDat_1_sf[!(modDat_1_sf$uniqueID %in% obsToRemove$uniqueID),]
modDat_1_s <- modDat_1_s[!(modDat_1_s$uniqueID %in% obsToRemove$uniqueID),]
}
#
# ggplot() +
# geom_sf(data = cropped_states[cropped_states$NAME == "Louisiana",]) +
# geom_sf(data = obs_LA_1, col = "black") +
# geom_sf(data = modDat_1_sf, col = "red", pch = 20) +
# $geom_hline(aes(yintercept = -590062))
#ylim(c(-1390431, -1222745)) +
#xlim(c(730439.1, 1042196))
}## do a pca of climate across level 2 ecoregions
pca <- prcomp(modDat_1_s[,paste0(prednames)])
library(factoextra)
(fviz_pca_ind(pca, habillage = modDat_1_s$NA_L2NAME, label = "none", addEllipses = TRUE, ellipse.level = .95, ggtheme = theme_minimal(), alpha.ind = .1))
if (ecoregion == "shrubGrass") {
print("We'll combine the 'Mediterranean California' and 'Western Sierra Madre Piedmont' ecoregions (into 'Mediterranean Piedmont'). We'll also combine `Tamaulipas-Texas semiarid plain,' 'Texas-Lousiana Coastal plain,' and 'South Central semiarid prairies' ecoregions (into (`Semiarid plain and prairies`)." )
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MEDITERRANEAN CALIFORNIA", "WESTERN SIERRA MADRE PIEDMONT"), "NA_L2NAME"] <- "MEDITERRANEAN PIEDMONT"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("TAMAULIPAS-TEXAS SEMIARID PLAIN", "TEXAS-LOUISIANA COASTAL PLAIN", "SOUTH CENTRAL SEMIARID PRAIRIES"), "NA_L2NAME"] <- "SEMIARID PLAIN AND PRAIRIES"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MARINE WEST COAST FOREST", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "WESTERN CORDILLERA AND WEST COAST FOREST"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MEDITERRANEAN CALIFORNIA", "UPPER GILA MOUNTAINS"), "NA_L2NAME"] <- "MEDITERRANEAN CALIFORNIA AND UPPER GILA MOUNTAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD PLAINS", "OZARK/OUACHITA-APPALACHIAN FORESTS"), "NA_L2NAME"] <- "OZARK/OUACHITA-APPALACHIAN FORESTS AND MIXED WOOD PLAINS"
#////
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MEDITERRANEAN CALIFORNIA", "WESTERN SIERRA MADRE PIEDMONT"), "NA_L2NAME"] <- "MEDITERRANEAN PIEDMONT"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("TAMAULIPAS-TEXAS SEMIARID PLAIN", "TEXAS-LOUISIANA COASTAL PLAIN", "SOUTH CENTRAL SEMIARID PRAIRIES"), "NA_L2NAME"] <- "SEMIARID PLAIN AND PRAIRIES"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MARINE WEST COAST FOREST", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "WESTERN CORDILLERA AND WEST COAST FOREST"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MEDITERRANEAN CALIFORNIA", "UPPER GILA MOUNTAINS"), "NA_L2NAME"] <- "MEDITERRANEAN CALIFORNIA AND UPPER GILA MOUNTAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD PLAINS", "OZARK/OUACHITA-APPALACHIAN FORESTS"), "NA_L2NAME"] <- "OZARK/OUACHITA-APPALACHIAN FORESTS AND MIXED WOOD PLAINS"
if (response == "CAMCover") {
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MEDITERRANEAN PIEDMONT", "SEMIARID PLAIN AND PRAIRIES"), "NA_L2NAME"] <- "SEMIARID PLAIN AND PRAIRIES AND PIEDMONT"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MEDITERRANEAN PIEDMONT", "SEMIARID PLAIN AND PRAIRIES"), "NA_L2NAME"] <- "SEMIARID PLAIN AND PRAIRIES AND PIEDMONT"
}
} else if (ecoregion == "CONUS") {
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("EVERGLADES", "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"), "NA_L2NAME"] <- "EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MARINE WEST COAST FOREST", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "WESTERN CORDILLERA AND WEST COAST FOREST"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD PLAINS", "OZARK/OUACHITA-APPALACHIAN FORESTS"), "NA_L2NAME"] <- "OZARK/OUACHITA-APPALACHIAN FORESTS AND MIXED WOOD PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MEDITERRANEAN CALIFORNIA", "UPPER GILA MOUNTAINS"), "NA_L2NAME"] <- "MEDITERRANEAN CALIFORNIA AND UPPER GILA MOUNTAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("OZARK/OUACHITA-APPALACHIAN FORESTS AND MIXED WOOD PLAINS", "SOUTHEASTERN USA PLAINS", "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS", "EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN AND MIXED WOOD PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("SOUTH CENTRAL SEMIARID PRAIRIES", "TEXAS-LOUISIANA COASTAL PLAIN"), "NA_L2NAME"] <- "SOUTH CENTRAL SEMIARID PRAIRIES"
#///
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("EVERGLADES", "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"), "NA_L2NAME"] <- "EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MARINE WEST COAST FOREST", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "WESTERN CORDILLERA AND WEST COAST FOREST"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD PLAINS", "OZARK/OUACHITA-APPALACHIAN FORESTS"), "NA_L2NAME"] <- "OZARK/OUACHITA-APPALACHIAN FORESTS AND MIXED WOOD PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MEDITERRANEAN CALIFORNIA", "UPPER GILA MOUNTAINS"), "NA_L2NAME"] <- "MEDITERRANEAN CALIFORNIA AND UPPER GILA MOUNTAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("OZARK/OUACHITA-APPALACHIAN FORESTS AND MIXED WOOD PLAINS", "SOUTHEASTERN USA PLAINS", "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS", "EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN AND MIXED WOOD PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("SOUTH CENTRAL SEMIARID PRAIRIES", "TEXAS-LOUISIANA COASTAL PLAIN"), "NA_L2NAME"] <- "SOUTH CENTRAL SEMIARID PRAIRIES"
} else if (ecoregion == "forest" & response != "CAMCover") {
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS", "EVERGLADES"), "NA_L2NAME"] <- "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS AND EVERGLADES"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("UPPER GILA MOUNTAINS", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS\tAND EVERGLADES", "SOUTHEASTERN USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN USA PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("ATLANTIC HIGHLANDS", "OZARK/OUACHITA-APPALACHIAN FORESTS"), "NA_L2NAME"] <- "HIGHLANDS AND APPALACHIAN FORESTS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("CENTRAL USA PLAINS", "MIXED WOOD PLAINS"), "NA_L2NAME"] <- "CENTRAL AND MIXED WOOD PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD SHIELD", "CENTRAL AND MIXED WOOD PLAINS"), "NA_L2NAME"] <- "CENTRAL AND MIXED WOOD PLAINS AND MIXED WOOD SHIELD"
## divide southeastern US plains into two regions, since it's by far the largest group
modDat_1_s[modDat_1_s$NA_L2NAME == "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS" &
modDat_1_s$Long < -966595#-1773969
, "NA_L2NAME"] <- "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS 1"
modDat_1_s[modDat_1_s$NA_L2NAME == "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS", "NA_L2NAME"] <- "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS 2"
#///
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS", "EVERGLADES"), "NA_L2NAME"] <- "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS AND EVERGLADES"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("UPPER GILA MOUNTAINS", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS\tAND EVERGLADES", "SOUTHEASTERN USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN USA PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("ATLANTIC HIGHLANDS", "OZARK/OUACHITA-APPALACHIAN FORESTS"), "NA_L2NAME"] <- "HIGHLANDS AND APPALACHIAN FORESTS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("CENTRAL USA PLAINS", "MIXED WOOD PLAINS"), "NA_L2NAME"] <- "CENTRAL AND MIXED WOOD PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD SHIELD", "CENTRAL AND MIXED WOOD PLAINS"), "NA_L2NAME"] <- "CENTRAL AND MIXED WOOD PLAINS AND MIXED WOOD SHIELD"
## divide southeastern US plains into two regions, since it's by far the largest group
modDat_1_sf[modDat_1_sf$NA_L2NAME == "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS" &
st_coordinates(modDat_1_sf)[,1] < -966595#-1773969
, ]$NA_L2NAME <- "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS 1"
modDat_1_sf[modDat_1_sf$NA_L2NAME == "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS", "NA_L2NAME"] <- "WESTERN CORDILLERA AND UPPER GILA MOUNTAINS 2"
} else if (ecoregion == "forest" & response == "CAMCover") {
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("OZARK/OUACHITA-APPALACHIAN FORESTS", "SOUTHEASTERN USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN PLAINS AND APPALACHIAN FORESTS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MARINE WEST COAST FOREST", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "MARINE WEST COAST AND WESTERN CORDILLERA"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD PLAINS", "SOUTHEASTERN PLAINS AND APPALACHIAN FORESTS"), "NA_L2NAME"] <- "SOUTHEASTERN PLAINS AND APPALACHIAN FORESTS"
#///
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("OZARK/OUACHITA-APPALACHIAN FORESTS", "SOUTHEASTERN USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN PLAINS AND APPALACHIAN FORESTS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MARINE WEST COAST FOREST", "WESTERN CORDILLERA"), "NA_L2NAME"] <- "MARINE WEST COAST AND WESTERN CORDILLERA"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD PLAINS", "SOUTHEASTERN PLAINS AND APPALACHIAN FORESTS"), "NA_L2NAME"] <- "SOUTHEASTERN PLAINS AND APPALACHIAN FORESTS"
} else if (ecoregion == "eastForest") {
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("EVERGLADES", "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"), "NA_L2NAME"] <- "EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD PLAINS","CENTRAL USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN AND CENTRAL USA PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD SHIELD", "ATLANTIC HIGHLANDS"), "NA_L2NAME"] <- "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD PLAINS", "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD"), "NA_L2NAME"] <- "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD AND PLAINS"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("SOUTHEASTERN AND CENTRAL USA PLAINS", "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD AND PLAINS"), "NA_L2NAME"] <- "PLAINS AND HIGHLANDS AND SHIELD"
modDat_1_s[modDat_1_s$NA_L2NAME %in% c("EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS", "SOUTHEASTERN USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN PLAINS AND COAST"
# #////
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("EVERGLADES", "MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"), "NA_L2NAME"] <- "EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD PLAINS","CENTRAL USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN AND CENTRAL USA PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD SHIELD", "ATLANTIC HIGHLANDS"), "NA_L2NAME"] <- "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD PLAINS", "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD"), "NA_L2NAME"] <- "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD AND PLAINS"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("SOUTHEASTERN AND CENTRAL USA PLAINS", "ATLANTIC HIGHLANDS AND MIXED WOOD SHIELD AND PLAINS"), "NA_L2NAME"] <- "PLAINS AND HIGHLANDS AND SHIELD"
modDat_1_sf[modDat_1_sf$NA_L2NAME %in% c("EVERGLADES MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS", "SOUTHEASTERN USA PLAINS"), "NA_L2NAME"] <- "SOUTHEASTERN PLAINS AND COAST"
## divide southeastern US plains into two regions, since it's by far the largest group
# modDat_1_s[modDat_1_s$NA_L2NAME == "SOUTHEASTERN USA PLAINS" &
# modDat_1_s$Lat < -590062, "NA_L2NAME"] <- "SOUTHEASTERN USA PLAINS 1"
# modDat_1_s[modDat_1_s$NA_L2NAME == "SOUTHEASTERN USA PLAINS", #&
# # modDat_1_s$Lat < -590062,
# "NA_L2NAME"] <- "SOUTHEASTERN USA PLAINS 2"
# ## divide southeastern US plains into two regions, since it's by far the largest group
# modDat_1_s[modDat_1_s$NA_L2NAME == "OZARK/OUACHITA-APPALACHIAN FORESTS" &
# modDat_1_s$Long < 854862.2, "NA_L2NAME"] <- "OZARK/OUACHITA-APPALACHIAN FORESTS 1"
# modDat_1_s[modDat_1_s$NA_L2NAME == "OZARK/OUACHITA-APPALACHIAN FORESTS" &
# modDat_1_s$Long >= 854862.2, "NA_L2NAME"] <- "OZARK/OUACHITA-APPALACHIAN FORESTS 2"
}## [1] "We'll combine the 'Mediterranean California' and 'Western Sierra Madre Piedmont' ecoregions (into 'Mediterranean Piedmont'). We'll also combine `Tamaulipas-Texas semiarid plain,' 'Texas-Lousiana Coastal plain,' and 'South Central semiarid prairies' ecoregions (into (`Semiarid plain and prairies`)."# make a table of n for each region
modDat_1_s %>%
group_by(NA_L2NAME) %>%
dplyr::summarize("Number_Of_Observations" = length(NA_L2NAME)) %>%
rename("Level_2_Ecoregion" = NA_L2NAME)%>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) | Level_2_Ecoregion | Number_Of_Observations |
|---|---|
| COLD DESERTS | 65580 |
| MEDITERRANEAN PIEDMONT | 5752 |
| SEMIARID PLAIN AND PRAIRIES | 25075 |
| TEMPERATE PRAIRIES | 3021 |
| WARM DESERTS | 18170 |
| WEST-CENTRAL SEMIARID PRAIRIES | 19494 |
Then, look at the spatial distribution and environmental characteristics of the grouped ecoregions
map1 <- ggplot() +
geom_sf(data=cropped_states,fill='white') +
geom_sf(data=modDat_1_sf#[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD PLAINS"),]
,
aes(fill=as.factor(NA_L2NAME)),linewidth=0.5,alpha=0.5) +
geom_point(data=modDat_1_s#[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD PLAINS"),]
,
alpha=0.5,
aes(x = Long, y = Lat, color=as.factor(NA_L2NAME)), alpha = .1) +
#scale_fill_okabeito() +
#scale_color_okabeito() +
# theme_default() +
theme(legend.position = 'none') +
labs(title = "Level 2 Ecoregions as spatial blocks")
hull <- modDat_1_sf %>%
ungroup() %>%
group_by(NA_L2NAME) %>%
slice(chull(tmean, prcp))
plot1<-ggplot(data=modDat_1_sf,aes(x=tmean,y=prcp)) +
geom_polygon(data = hull, alpha = 0.25,aes(fill=NA_L2NAME) )+
geom_point(aes(group=NA_L2NAME,color=NA_L2NAME),alpha=0.25) +
theme_minimal() + xlab("Annual Average T_mean - long-term average") +
ylab("Annual Average Precip - long-term average") #+
#scale_color_okabeito() +
#scale_fill_okabeito()
plot2<-ggplot(data=modDat_1_sf %>%
pivot_longer(cols=tmean:prcp),
aes(x=value,group=name)) +
# geom_polygon(data = hull, alpha = 0.25,aes(fill=fold) )+
geom_density(aes(group=NA_L2NAME,fill=NA_L2NAME),alpha=0.25) +
theme_minimal() +
facet_wrap(~name,scales='free')# +
#scale_color_okabeito() +
#scale_fill_okabeito()
library(patchwork)
(combo <- (map1+plot1)/plot2) 
#
# ggplot(data = modDat_1_s) +
# geom_density(aes(ShrubCover_transformed, col = NA_L2NAME)) +
# xlim(c(0,100))Fit a global model with all of the data
First, fit a LASSO regression model using the glmnet R package
- This regression is a Gamma glm with a log link
- Use cross validation across level 2 ecoregions to tune the lambda parameter in the LASSO model
- this model is fit to using the scaled weather/climate/soils variables
- this list of possible predictors includes:
- main effects
- interactions between all soils variables
- interactions between climate and weather variables
- transformed main effects (squared, log-transformed (add a uniform integer – 20– to all variables prior to log-transformation), square root-transformed (add a uniform integer – 20– to all variables prior to log-transformation))
Get rid of transformed predictions and interactions that are correlated
# get first pass of names correlated variables
X_df <- X %>%
as.data.frame() %>%
dplyr::select(-'(Intercept)')
corrNames_i <- X_df %>%
cor() %>%
caret::findCorrelation(cutoff = .7, verbose = FALSE, names = TRUE, exact = TRUE)
# remove those names that are untransformed main effects
# vector of columns to remove
badNames <- corrNames_i[!(corrNames_i %in% prednames)]
while (sum(!(corrNames_i %in% prednames))>0) {
corrNames_i <- X_df %>%
dplyr::select(-badNames) %>%
cor() %>%
caret::findCorrelation(cutoff = .7, verbose = FALSE, names = TRUE, exact = TRUE)
# update the vector of names to remove
badNames <- unique(c(badNames, corrNames_i[!(corrNames_i %in% prednames)]))
}
## see if there are any correlated variables left (would be all main effects at this point)
# if there are, step through and remove the variable that each is most correlated with
if (length(corrNames_i)>1) {
for (i in 1:length(corrNames_i)) {
X_i <- X_df %>%
dplyr::select(-badNames)
if (corrNames_i[i] %in% names(X_i)) {
corMat_i <- cor(x = X_i[corrNames_i[i]], y = X_i %>% dplyr::select(-corrNames_i[i]))
badNames_i <- colnames(corMat_i)[abs(corMat_i)>=.7]
# if there are any predictors in the 'badNames_i', remove them from this list
if (length(badNames_i) > 0 & sum(c(badNames_i %in% prednames))>0) {
badNames_i <- badNames_i[!(badNames_i %in% prednames)]
}
badNames <- unique(c(badNames, badNames_i))
}
}
}
## update the X matrix to exclude these correlated variables
X <- X[,!(colnames(X) %in% badNames)]if (autoKfold == FALSE) {
# get the ecoregions for training lambda
train_eco <- modDat_1_s$NA_L2NAME#[train]
# Fit model -----------------------------------------------
# specify leave-one-year-out cross-validation
my_folds <- as.numeric(as.factor(train_eco))
if (run == TRUE) {
# set up parallel processing
library(doMC)
# this computer has 16 cores (parallel::detectCores())
registerDoMC(cores = 8)
fit <- cv.glmnet(
x = X[,2:ncol(X)],
y = y,
family = stats::Gamma(link = "log"),
keep = FALSE,
alpha = 1, # 0 == ridge regression, 1 == lasso, 0.5 ~~ elastic net
#lambda = lambdas,
nlambda = 50,
type.measure="mse",
#penalty.factor = pen_facts,
foldid = my_folds,
#thresh = thresh,
standardize = FALSE, ## scales variables prior to the model sequence... coefficients are always returned on the original scale
parallel = TRUE#,
#relax = ifelse(response == "ShrubCover", yes = TRUE, no = FALSE)
)
base::saveRDS(fit, paste0("../ModelFitting/models/", response, "_globalLASSOmod_gammaLogLink_",ecoregion, "_noTLP_",removeTLP,".rds"))
} else {
fit <- readRDS(paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/", response, "_globalLASSOmod_gammaLogLink_",ecoregion, "_noTLP_",removeTLP,".rds"))
}
} else if (autoKfold == TRUE) {
if (run == TRUE) {
# set up parallel processing
library(doMC)
# this computer has 16 cores (parallel::detectCores())
registerDoMC(cores = 8)
fit <- cv.glmnet(
x = X[,2:ncol(X)],
y = y,
family = stats::Gamma(link = "log"),
keep = FALSE,
alpha = .5, # 0 == ridge regression, 1 == lasso, 0.5 ~~ elastic net
lambda = lambdas,
relax = ifelse(response == "ShrubCover", yes = TRUE, no = FALSE),
#nlambda = 100,
type.measure="mse",
#penalty.factor = pen_facts,
#foldid = my_folds,
#thresh = thresh,
standardize = FALSE, ## scales variables prior to the model sequence... coefficients are always returned on the original scale
parallel = TRUE
)
base::saveRDS(fit, paste0("../ModelFitting/models/", response, "_globalLASSOmod_gammaLogLink_",ecoregion, "_noTLP_",removeTLP,"_kFoldDefault.rds"))
} else {
fit <- readRDS(paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/", response, "_globalLASSOmod_gammaLogLink_",ecoregion, "_noTLP_",removeTLP,"_kFoldDefault.rds"))
}
}
# assess model fit
# assess.glmnet(fit$fit.preval, #newx = X[,2:293],
# newy = y, family = stats::Gamma(link = "log"))
# save the minimum lambda
best_lambda <- fit$lambda.min
# save the lambda for the most regularized model w/ an MSE that is still 1SE w/in the best lambda model
lambda_1SE <- fit$lambda.1se
# save the lambda for the most regularized model w/ an MSE that is still .5SE w/in the best lambda model
lambda_halfSE <- best_lambda + ((lambda_1SE - best_lambda)/2)
print(fit) ##
## Call: cv.glmnet(x = X[, 2:ncol(X)], y = y, type.measure = "mse", foldid = my_folds, keep = FALSE, parallel = TRUE, family = stats::Gamma(link = "log"), alpha = 1, nlambda = 50, standardize = FALSE)
##
## Measure: Mean-Squared Error
##
## Lambda Index Measure SE Nonzero
## min 0.002468 27 224.1 49.87 103
## 1se 0.023544 15 271.0 60.87 23 plot(fit)
Now, we need to do stability selection to ensure the coefficients that are being chosen with each lambda are stable
## stability selection for best lambda model
# setup params
p <- ncol(X[,2:ncol(X)]) # of parameters
n <- length(y) # of observations
n_iter <- 100 # number of subsamples
sample_frac <- 0.75 # fraction of data to subsample
lambda_val <- best_lambda # fixed lambda value (could be chosen via CV)
# Track selection
selection_counts <- matrix(0, nrow = p, ncol = 1)
for (i in 1:n_iter) {
# Subsample rows
sample_idx <- sample(1:n, size = floor(sample_frac * n), replace = FALSE)
X_sub <- X[sample_idx, ]
y_sub <- y[sample_idx]
# Fit Lasso model
fit_stab_i <- glmnet(x = X_sub[,2:ncol(X_sub)], y = y_sub,
family = stats::Gamma(link = "log"),
alpha = 1, lambda = lambda_val, standardize = FALSE)
# Get non-zero coefficients (excluding intercept)
selected <- which(as.vector(coef(fit_stab_i))[-1] != 0)
selection_counts[selected] <- selection_counts[selected] + 1
}
# Convert counts to selection probabilities (the probability that a variable is selected over 100 iterations)
selection_prob <- selection_counts / n_iter
selection_prob_df <- data.frame(
VariableNumber = paste0("X", 1:p),
VariableName = rownames(coef(fit_stab_i))[2:(p+1)],
SelectionProb = as.vector(selection_prob)
)
# get those variables that are selected in ≥70% of subsets (Meinshausen and Bühlmann, 2010)
bestLambda_coef <- selection_prob_df[selection_prob_df$SelectionProb>=.7, c("VariableName", "SelectionProb")]
#//////
# stability selection for 1se lambda model
lambda_val <- lambda_1SE # fixed lambda value (could be chosen via CV)
# Track selection
selection_counts <- matrix(0, nrow = p, ncol = 1)
for (i in 1:n_iter) {
# Subsample rows
sample_idx <- sample(1:n, size = floor(sample_frac * n), replace = FALSE)
X_sub <- X[sample_idx, ]
y_sub <- y[sample_idx]
# Fit Lasso model
fit_stab_i <- glmnet(x = X_sub[,2:ncol(X_sub)], y = y_sub,
family = stats::Gamma(link = "log"),
alpha = 1, lambda = lambda_val, standardize = FALSE)
# Get non-zero coefficients (excluding intercept)
selected <- which(as.vector(coef(fit_stab_i))[-1] != 0)
selection_counts[selected] <- selection_counts[selected] + 1
}
# Convert counts to selection probabilities (the probability that a variable is selected over 100 iterations)
selection_prob <- selection_counts / n_iter
selection_prob_df <- data.frame(
VariableNumber = paste0("X", 1:p),
VariableName = rownames(coef(fit_stab_i))[2:(p+1)],
SelectionProb = as.vector(selection_prob)
)
# get those variables that are selected in ≥70% of subsets (Meinshausen and Bühlmann, 2010)
seLambda_coef <- selection_prob_df[selection_prob_df$SelectionProb>=.7, c("VariableName", "SelectionProb")]
#//////
# stability selection for half se lambda model
lambda_val <- lambda_halfSE # fixed lambda value (could be chosen via CV)
# Track selection
selection_counts <- matrix(0, nrow = p, ncol = 1)
for (i in 1:n_iter) {
# Subsample rows
sample_idx <- sample(1:n, size = floor(sample_frac * n), replace = FALSE)
X_sub <- X[sample_idx, ]
y_sub <- y[sample_idx]
# Fit Lasso model
fit_stab_i <- glmnet(x = X_sub[,2:ncol(X_sub)], y = y_sub,
family = stats::Gamma(link = "log"),
alpha = 1, lambda = lambda_val, standardize = FALSE)
# Get non-zero coefficients (excluding intercept)
selected <- which(as.vector(coef(fit_stab_i))[-1] != 0)
selection_counts[selected] <- selection_counts[selected] + 1
}
# Convert counts to selection probabilities (the probability that a variable is selected over 100 iterations)
selection_prob <- selection_counts / n_iter
selection_prob_df <- data.frame(
VariableNumber = paste0("X", 1:p),
VariableName = rownames(coef(fit_stab_i))[2:(p+1)],
SelectionProb = as.vector(selection_prob)
)
# get those variables that are selected in ≥70% of subsets (Meinshausen and Bühlmann, 2010)
halfseLambda_coef <- selection_prob_df[selection_prob_df$SelectionProb>=.7, c("VariableName", "SelectionProb")]If prompted to do so by the input arguments, remove any predictors that are for weather anomalies whose corresponding climate predictor is not included in the model
if (trimAnom == TRUE) {
# get unique predictors
bestLamb_all <- bestLambda_coef$VariableName %>% #[str_detect(bestLambda_coef$VariableName, pattern = "_anom_s")] %>%
str_remove(pattern = "I\\(") %>%
str_remove(pattern = "\\^2\\)") %>%
str_split(pattern = ":", simplify = TRUE)
if (ncol(bestLamb_all) == 1) {
bestLamb_all <- unique(bestLamb_all)
} else {
bestLamb_all <- c(bestLamb_all[bestLamb_all[,1] !="",1], bestLamb_all[bestLamb_all[,2] !="",2]) %>%
unique()
}
# get anomalies
bestLamb_anom <- bestLamb_all[bestLamb_all %in% prednames_weath]
# get climate
bestLamb_clim <- bestLamb_all[bestLamb_all %in% prednames_clim]
# which anomalies are present in the predictors, but their corresponding climate variable isn't
bestLamb_anomsWithMissingClim <- bestLamb_anom[!(str_remove(bestLamb_anom, "_anom") %in% bestLamb_clim)]
# remove anomalies (and all interaction terms w/ those anomalies) from the predictor list
if (length(bestLamb_anomsWithMissingClim) != 0) {
bestLambda_coef_NEW <- bestLambda_coef
for (i in 1:length(bestLamb_anomsWithMissingClim)) {
bestLambda_coef_NEW <- bestLambda_coef_NEW[!str_detect(bestLambda_coef_NEW$VariableName, pattern = bestLamb_anomsWithMissingClim[i]),]
}
bestLambda_coef <- bestLambda_coef_NEW
}
#//// 1 se lambda model
if (nrow(seLambda_coef) !=0) {
# get unique predictors
seLamb_all <- seLambda_coef$VariableName %>% #[str_detect(seLambda_coef$VariableName, pattern = "_anom_s")] %>%
str_remove(pattern = "I\\(") %>%
str_remove(pattern = "_s\\^2\\)") %>%
str_split(pattern = ":", simplify = TRUE)
if (ncol(seLamb_all) == 1) {
seLamb_all <- unique(seLamb_all)
} else {
seLamb_all <- c(seLamb_all[seLamb_all[,1] !="",1], seLamb_all[seLamb_all[,2] !="",2]) %>%
unique()
}
# get anomalies
seLamb_anom <- seLamb_all[seLamb_all %in% prednames_weath]
# get climate
seLamb_clim <- seLamb_all[seLamb_all %in% prednames_clim]
# which anomalies are present in the predictors, but their corresponding climate variable isn't
seLamb_anomsWithMissingClim <- seLamb_anom[!(str_remove(seLamb_anom, "_anom") %in% seLamb_clim)]
# remove anomalies (and all interaction terms w/ those anomalies) from the predictor list
if (length(seLamb_anomsWithMissingClim) != 0) {
seLambda_coef_NEW <- seLambda_coef
for (i in 1:length(seLamb_anomsWithMissingClim)) {
seLambda_coef_NEW <- seLambda_coef_NEW[!str_detect(seLambda_coef_NEW$VariableName, pattern = seLamb_anomsWithMissingClim[i]),]
}
seLambda_coef <- seLambda_coef_NEW
}
}
#//// 1 se lambda model
if (nrow(halfseLambda_coef) !=0) {
# get unique predictors
halfseLamball<- halfseLambda_coef$VariableName %>% #[str_detect(halfseLambda_coef$VariableName, pattern = "_anom_s")] %>%
str_remove(pattern = "I\\(") %>%
str_remove(pattern = "_s\\^2\\)") %>%
str_split(pattern = ":", simplify = TRUE)
if (ncol(halfseLamball) == 1) {
halfseLamball <- unique(halfseLamball)
} else {
halfseLamball <- c(halfseLamball[halfseLamball[,1] !="",1], halfseLamball[halfseLamball[,2] !="",2]) %>%
unique()
}
# get anomalies
halfseLambanom <- halfseLamball[halfseLamball %in% prednames_weath]
# get climate
halfseLambclim <- halfseLamball[halfseLamball %in% prednames_clim]
# which anomalies are present in the predictors, but their corresponding climate variable isn't
halfseLambanomsWithMissingClim <- halfseLambanom[!(str_remove(halfseLambanom, "_anom_s") %in% halfseLambclim)]
# remove anomalies (and all interaction terms w/ those anomalies) from the predictor list
##
if (length(halfseLambanomsWithMissingClim) != 0) {
halfseLambda_coef_NEW <- halfseLambda_coef
for (i in 1:length(halfseLambanomsWithMissingClim)) {
halfseLambda_coef_NEW <- halfseLambda_coef_NEW[!str_detect(halfseLambda_coef_NEW$VariableName, pattern = halfseLambanomsWithMissingClim[i]),]
}
halfseLambda_coef <- halfseLambda_coef_NEW
}
}
##
}Then, fit regular glm models (Gamma glm with a log link), first using the coefficients from the ‘best’ lambda identified in the LASSO model, and then using the coefficients from the ‘1SE’ lambda and then the ‘1/2SE’ lambda values identified from the LASSO (this is the value of lambda such that the cross validation error is within 1 standard error of the minimum).
## fit w/ the identified coefficients from the 'best' lambda, but using the glm function
mat_glmnet_best <- bestLambda_coef$VariableName
if (length(mat_glmnet_best) == 0) {
f_glm_bestLambda <- as.formula(paste0(response_t, "~ 1"))
} else {
f_glm_bestLambda <- as.formula(paste0(response_t, " ~ ", paste0(mat_glmnet_best, collapse = " + ")))
}
fit_glm_bestLambda <- glm(data = modDat_1_s
, formula = f_glm_bestLambda, family = stats::Gamma(link = "log"))
## fit w/ the identified coefficients from the '1se' lambda, but using the glm function
mat_glmnet_1se <- seLambda_coef$VariableName
if (length(mat_glmnet_1se) == 0) {
f_glm_1se <- as.formula(paste0(response_t, "~ 1"))
} else {
f_glm_1se <- as.formula(paste0(response_t, " ~ ", paste0(mat_glmnet_1se, collapse = " + ")))
}
fit_glm_se <- glm(data = modDat_1_s, formula = f_glm_1se,
family = stats::Gamma(link = "log"))
## fit w/ the identified coefficients from the '.5se' lambda, but using the glm function
mat_glmnet_halfse <- halfseLambda_coef$VariableName
if (length(mat_glmnet_halfse) == 0) {
f_glm_halfse <- as.formula(paste0(response_t, "~ 1"))
} else {
f_glm_halfse <- as.formula(paste0(response_t, " ~ ", paste0(mat_glmnet_halfse, collapse = " + ")))
}
fit_glm_halfse <- glm(data = modDat_1_s, formula = f_glm_halfse,
family = stats::Gamma(link = "log"))
## save models
if (trimAnom == TRUE) {
saveRDS(fit_glm_bestLambda, paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/",response,"_",ecoregion, "_noTLP_",removeTLP,"_trimAnom_bestLambdaGLM.rds"))
saveRDS(fit_glm_halfse, paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/",response,"_",ecoregion, "_noTLP_",removeTLP,"_trimAnom_halfSELambdaGLM.rds"))
saveRDS(fit_glm_se, paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/",response,"_",ecoregion, "_noTLP_",removeTLP,"_trimAnom_oneSELambdaGLM.rds"))
} else {
saveRDS(fit_glm_bestLambda, paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/",response,"_",ecoregion, "_noTLP_",removeTLP,"_bestLambdaGLM.rds"))
saveRDS(fit_glm_halfse, paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/",response,"_",ecoregion, "_noTLP_",removeTLP,"_halfSELambdaGLM.rds"))
saveRDS(fit_glm_se, paste0("/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Analysis/VegComposition/ModelFitting/models/",response,"_",ecoregion, "_noTLP_",removeTLP,"_oneSELambdaGLM.rds"))
}Then, we predict (on the training set) using both of these models (best lambda and 1se lambda)
## predict on the test data
# lasso model predictions with the optimal lambda
optimal_pred <- predict(fit_glm_bestLambda, newx=X[,2:ncol(X)], type = "response") - 2
optimal_pred_1se <- predict(fit_glm_se, newx=X[,2:ncol(X)], type = "response") - 2
optimal_pred_halfse <- predict(fit_glm_halfse, newx = X[,2:ncol(X)], type = "response") - 2
null_fit <- glm(#data = data.frame("y" = y, X[,paste0(prednames, "_s")]),
formula = y ~ 1, family = stats::Gamma(link = "log"))
null_pred <- predict(null_fit, newdata = as.data.frame(X), type = "response"
) - 2
## snap values above 100 and below 0 to be 100 or z
optimal_pred[optimal_pred>100] <- 100
optimal_pred[optimal_pred<0] <- 0
optimal_pred_1se[optimal_pred_1se>100] <- 100
optimal_pred_1se[optimal_pred_1se<0] <- 0
optimal_pred_halfse[optimal_pred_halfse>100] <- 100
optimal_pred_halfse[optimal_pred_halfse<0] <- 0
# save data
fullModOut <- list(
"modelObject" = fit,
"nullModelObject" = null_fit,
"modelPredictions" = data.frame(#ecoRegion_holdout = rep(test_eco,length(y)),
obs=y-2,
pred_opt=optimal_pred,
pred_opt_se = optimal_pred_1se,
pred_opt_halfse = optimal_pred_halfse,
pred_null=null_pred#,
#pred_nopenalty=nopen_pred
))
# # calculate correlations between null and optimal model
# my_cors <- c(cor(optimal_pred, c(y)),
# cor(optimal_pred_1se, c(y)),
# cor(null_pred, c(y))
# )
#
# # calculate mse between null and optimal model
# my_mse <- c(mean((fullModOut$modelPredictions$pred_opt - c(y))^2) ,
# mean((fullModOut$modelPredictions$pred_opt_se - c(y))^2) ,
# mean((fullModOut$modelPredictions$pred_null - c(y))^2)#,
# #mean((obs_pred$pred_nopenalty - obs_pred$obs)^2)
# )
ggplot() +
geom_point(aes(X[,2], fullModOut$modelPredictions$obs), col = "black", alpha = .1) +
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_opt), col = "red", alpha = .1) + ## predictions w/ the CV model
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_opt_halfse), col = "orange", alpha = .1) + ## predictions w/ the CV model (.5se lambda)
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_opt_se), col = "green", alpha = .1) + ## predictions w/ the CV model (1se lambda)
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_null), col = "blue", alpha = .1) +
labs(title = "A rough comparison of observed and model-predicted values",
subtitle = "black = observed values \n red = predictions from 'best lambda' model \n orange = predictions for '1/2se' lambda model \n green = predictions from '1se' lambda model \n blue = predictions from null model") +
xlab(colnames(X)[2])
#ylim(c(0,200))The internal cross-validation process to fit the global LASSO model
identified an optimal lambda value (regularization parameter) of
r{print(best_lambda)}. The lambda value such that the cross
validation error is within 1 standard error of the minimum (“1se
lambda”) was `r{print(fit$lambda.1se)}`` . The following coefficients
were kept in each model:
# the coefficient matrix from the 'best model' -- find and print those coefficients that aren't 0 in a table
coef_glm_bestLambda <- coef(fit_glm_bestLambda) %>%
data.frame()
coef_glm_bestLambda$coefficientName <- rownames(coef_glm_bestLambda)
names(coef_glm_bestLambda)[1] <- "coefficientValue_bestLambda"
# coefficient matrix from the '1se' model
coef_glm_1se <- coef(fit_glm_se) %>%
data.frame()
coef_glm_1se$coefficientName <- rownames(coef_glm_1se)
names(coef_glm_1se)[1] <- "coefficientValue_1seLambda"
# coefficient matrix from the 'half se' model
coef_glm_halfse <- coef(fit_glm_halfse) %>%
data.frame()
coef_glm_halfse$coefficientName <- rownames(coef_glm_halfse)
names(coef_glm_halfse)[1] <- "coefficientValue_halfseLambda"
# add together
coefs <- full_join(coef_glm_bestLambda, coef_glm_halfse) %>%
full_join(coef_glm_1se) %>%
dplyr::select(coefficientName, coefficientValue_bestLambda,
coefficientValue_halfseLambda, coefficientValue_1seLambda)
globModTerms <- coefs[!is.na(coefs$coefficientValue_bestLambda), "coefficientName"]
## also, get the number of unique variables in each model
var_prop_pred <- paste0(response, "_pred")
response_vars <- c(response, var_prop_pred)
# for best lambda model
prednames_fig <- paste(str_split(globModTerms, ":", simplify = TRUE))
prednames_fig <- str_replace(prednames_fig, "I\\(", "")
prednames_fig <- str_replace(prednames_fig, "\\^2\\)", "")
prednames_fig <- unique(prednames_fig[prednames_fig>0])
prednames_fig <- prednames_fig
prednames_fig_num <- length(prednames_fig)
# for 1SE lambda model
globModTerms_1se <- coefs[!is.na(coefs$coefficientValue_1seLambda), "coefficientName"]
if (length(globModTerms_1se) == 1) {
prednames_fig_1se <- paste(str_split(globModTerms_1se, ":", simplify = TRUE))
prednames_fig_1se <- str_replace(prednames_fig_1se, "I\\(", "")
prednames_fig_1se <- str_replace(prednames_fig_1se, "\\^2\\)", "")
prednames_fig_1se <- unique(prednames_fig_1se[prednames_fig_1se>0])
prednames_fig_1se_num <- c(0)
} else {
prednames_fig_1se <- paste(str_split(globModTerms_1se, ":", simplify = TRUE))
prednames_fig_1se <- str_replace(prednames_fig_1se, "I\\(", "")
prednames_fig_1se <- str_replace(prednames_fig_1se, "\\^2\\)", "")
prednames_fig_1se <- unique(prednames_fig_1se[prednames_fig_1se>0])
prednames_fig_1se_num <- length(prednames_fig_1se)
}
# for 1/2SE lambda model
globModTerms_halfse <- coefs[!is.na(coefs$coefficientValue_halfseLambda), "coefficientName"]
if (length(globModTerms_halfse) == 1) {
prednames_fig_halfse <- paste(str_split(globModTerms_halfse, ":", simplify = TRUE))
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "I\\(", "")
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "\\^2\\)", "")
prednames_fig_halfse <- unique(prednames_fig_halfse[prednames_fig_halfse>0])
prednames_fig_halfse_num <- c(0)
} else {
prednames_fig_halfse <- paste(str_split(globModTerms_halfse, ":", simplify = TRUE))
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "I\\(", "")
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "\\^2\\)", "")
prednames_fig_halfse <- unique(prednames_fig_halfse[prednames_fig_halfse>0])
prednames_fig_halfse_num <- length(prednames_fig_halfse)
}
# make a table
kable(coefs, col.names = c("Coefficient Name", "Value from best lambda model",
"Value form 1/2 se lambda", "Value from 1se lambda model")
) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) | Coefficient Name | Value from best lambda model | Value form 1/2 se lambda | Value from 1se lambda model |
|---|---|---|---|
| (Intercept) | 1.9113785 | 2.2485226 | 2.3173549 |
| tmean | -0.4596256 | -0.1032718 | -0.1161970 |
| prcp | -0.7360529 | -1.2106122 | -1.1347561 |
| prcp_seasonality | -0.2262243 | NA | NA |
| prcpTempCorr | -0.0142011 | -0.1828975 | -0.0776833 |
| isothermality | 0.1976807 | 0.1441150 | 0.1702439 |
| annWatDef | 0.5294354 | NA | NA |
| sand | -0.0616214 | 0.0022038 | -0.0051807 |
| coarse | -0.1184932 | -0.1612023 | -0.2060836 |
| carbon | -1.0068380 | NA | NA |
| AWHC | 0.0496127 | 0.0481777 | NA |
| t_warm_anom | -0.0099409 | -0.0200370 | -0.0272136 |
| prcp_wet_anom | 0.0583020 | 0.0519317 | NA |
| precp_dry_anom | 0.0044418 | 0.0059351 | NA |
| prcp_seasonality_anom | -0.0501401 | -0.0446759 | NA |
| aboveFreezingMonth_anom | -0.0118032 | NA | NA |
| isothermality_anom | -0.0158462 | -0.0243061 | NA |
| VPD_mean_anom | -0.0190158 | NA | NA |
| frostFreeDays_5_anom | -0.0203617 | NA | NA |
| I(prcp^2) | 0.4938233 | 0.6907153 | 0.6045363 |
| I(prcpTempCorr^2) | -0.0280209 | -0.0759833 | -0.0642111 |
| I(isothermality^2) | -0.0064607 | -0.0217159 | -0.0255786 |
| I(t_warm_anom^2) | -0.0081991 | -0.0087300 | -0.0043039 |
| I(prcp_wet_anom^2) | 0.0060323 | NA | NA |
| I(prcpTempCorr_anom^2) | 0.0028571 | NA | NA |
| I(annWatDef_anom^2) | 0.0120932 | 0.0135121 | -0.0029140 |
| I(frostFreeDays_5_anom^2) | -0.0003803 | 0.0110755 | 0.0072493 |
| I(sand^2) | 0.0341849 | 0.0326556 | NA |
| I(coarse^2) | 0.0243219 | NA | NA |
| I(carbon^2) | 0.4261403 | NA | NA |
| prcp_wet_anom:aboveFreezingMonth_anom | 0.0059092 | NA | NA |
| annWatDef:prcp_seasonality_anom | 0.0185373 | NA | NA |
| annWatDef:precp_dry_anom | -0.0076947 | NA | NA |
| isothermality:annWatDef_anom | -0.0116696 | NA | NA |
| prcp_seasonality:annWatDef_anom | -0.0289292 | NA | NA |
| tmean:annWatDef_anom | -0.0374379 | NA | NA |
| VPD_mean_anom:annWatDef_anom | -0.0231598 | NA | NA |
| isothermality_anom:annWetDegDays_anom | -0.0068356 | NA | NA |
| prcp_seasonality_anom:annWetDegDays_anom | -0.0084609 | NA | NA |
| prcp_wet_anom:annWetDegDays_anom | 0.0064197 | 0.0124338 | NA |
| annWetDegDays_anom:prcpTempCorr_anom | 0.0045512 | NA | NA |
| t_warm_anom:annWetDegDays_anom | 0.0152830 | NA | NA |
| isothermality_anom:frostFreeDays_5_anom | -0.0026976 | NA | NA |
| prcp:frostFreeDays_5_anom | -0.0379225 | -0.0380976 | NA |
| prcp_seasonality:frostFreeDays_5_anom | -0.0103669 | NA | NA |
| prcpTempCorr:frostFreeDays_5_anom | -0.0219080 | -0.0233470 | NA |
| frostFreeDays_5_anom:tmax_anom | -0.0017223 | NA | NA |
| isothermality:isothermality_anom | -0.0138918 | NA | NA |
| prcp_seasonality:isothermality | -0.0564044 | NA | NA |
| isothermality:prcp_wet_anom | -0.0219456 | NA | NA |
| prcpTempCorr:isothermality | 0.0002160 | 0.0121292 | 0.1147207 |
| isothermality:tmax_anom | -0.0312831 | NA | NA |
| tmean:isothermality | -0.0303246 | -0.0437041 | NA |
| isothermality:VPD_min_anom | 0.0129508 | NA | NA |
| prcp_seasonality:isothermality_anom | 0.0098000 | NA | NA |
| prcp_seasonality_anom:isothermality_anom | -0.0000760 | NA | NA |
| t_warm_anom:isothermality_anom | 0.0062919 | NA | NA |
| isothermality_anom:tmax_anom | 0.0079246 | 0.0093182 | NA |
| isothermality_anom:VPD_min_anom | -0.0106656 | NA | NA |
| prcp:prcpTempCorr | -0.3640498 | -0.2920514 | NA |
| prcp:t_warm_anom | 0.0294714 | NA | NA |
| prcp_seasonality:prcp_wet_anom | -0.0149420 | NA | NA |
| prcp_seasonality:prcpTempCorr | -0.1159393 | NA | NA |
| prcp_seasonality:tmax_anom | 0.0237856 | NA | NA |
| prcp_seasonality:tmin_anom | 0.0030433 | NA | NA |
| prcp_wet_anom:prcp_seasonality_anom | -0.0056738 | NA | NA |
| prcpTempCorr:prcp_seasonality_anom | 0.0004103 | -0.0059726 | NA |
| prcp_seasonality_anom:prcpTempCorr_anom | 0.0124445 | 0.0125154 | NA |
| precp_dry_anom:prcp_seasonality_anom | 0.0072460 | NA | NA |
| t_warm_anom:prcp_seasonality_anom | -0.0075992 | -0.0190040 | -0.0178122 |
| prcp_seasonality_anom:VPD_mean_anom | 0.0124009 | NA | NA |
| prcp_wet_anom:tmax_anom | -0.0126649 | NA | NA |
| tmean:prcp_wet_anom | 0.0168096 | NA | NA |
| prcp_wet_anom:VPD_mean_anom | 0.0007446 | NA | NA |
| prcpTempCorr:prcpTempCorr_anom | 0.0208917 | 0.0208728 | NA |
| prcpTempCorr:t_warm_anom | -0.0118917 | -0.0033158 | -0.0181404 |
| tmean:prcpTempCorr | 0.2694333 | 0.1765317 | NA |
| prcpTempCorr:tmin_anom | -0.0267520 | -0.0022317 | -0.0106771 |
| prcpTempCorr:VPD_mean_anom | 0.0223628 | NA | NA |
| prcpTempCorr:VPD_min_anom | 0.0043279 | NA | NA |
| precp_dry_anom:prcpTempCorr_anom | 0.0034735 | NA | NA |
| t_warm_anom:prcpTempCorr_anom | -0.0085053 | NA | NA |
| precp_dry_anom:VPD_min_anom | -0.0030144 | NA | NA |
| t_warm_anom:VPD_mean_anom | -0.0111978 | -0.0081962 | NA |
| VPD_mean_anom:tmax_anom | -0.0058024 | NA | NA |
| tmean:tmin_anom | 0.0104119 | NA | NA |
| VPD_mean_anom:VPD_min_anom | -0.0051570 | -0.0107127 | NA |
| coarse:AWHC | 0.0255577 | NA | NA |
| sand:AWHC | 0.0331793 | 0.0318183 | NA |
| sand:coarse | -0.0337781 | 0.0014770 | NA |
| annWetDegDays_anom | NA | -0.0039020 | 0.0226043 |
| I(annWetDegDays_anom^2) | NA | -0.0024452 | 0.0021108 |
| prcpTempCorr:annWetDegDays_anom | NA | 0.0124838 | NA |
| prcpTempCorr:isothermality_anom | NA | 0.0128419 | NA |
| tmax_anom:tmin_anom | NA | 0.0005111 | NA |
| annWetDegDays_anom:prcp_wet_anom | NA | NA | 0.0065145 |
| t_warm_anom:tmin_anom | NA | NA | 0.0024177 |
| tmin_anom:tmax_anom | NA | NA | -0.0084779 |
# calculate RMSE of all models
RMSE_best <- yardstick::rmse(fullModOut$modelPredictions[,c("obs", "pred_opt")], truth = "obs", estimate = "pred_opt")$.estimate
RMSE_halfse <- yardstick::rmse(fullModOut$modelPredictions[,c("obs", "pred_opt_halfse")], truth = "obs", estimate = "pred_opt_halfse")$.estimate
RMSE_1se <- yardstick::rmse(fullModOut$modelPredictions[,c("obs", "pred_opt_se")], truth = "obs", estimate = "pred_opt_se")$.estimate
# calculate bias of all models
bias_best <- mean((fullModOut$modelPredictions$obs) - fullModOut$modelPredictions$pred_opt)
bias_halfse <- mean((fullModOut$modelPredictions$obs) - fullModOut$modelPredictions$pred_opt_halfse)
bias_1se <- mean((fullModOut$modelPredictions$obs) - fullModOut$modelPredictions$pred_opt_se)
uniqueCoeffs <- data.frame("Best lambda model" = c(RMSE_best, bias_best,
as.integer(length(globModTerms)-1), as.integer(prednames_fig_num),
as.integer(sum(prednames_fig %in% c(prednames_clim))),
as.integer(sum(prednames_fig %in% c(prednames_weath))),
as.integer(sum(prednames_fig %in% c(prednames_soils)))
),
"1/2 se lambda model" = c(RMSE_halfse, bias_halfse,
length(globModTerms_halfse)-1, prednames_fig_halfse_num,
sum(prednames_fig_halfse %in% c(prednames_clim)),
sum(prednames_fig_halfse %in% c(prednames_weath)),
sum(prednames_fig_halfse %in% c(prednames_soils))),
"1se lambda model" = c(RMSE_1se, bias_1se,
length(globModTerms_1se)-1, prednames_fig_1se_num,
sum(prednames_fig_1se %in% c(prednames_clim)),
sum(prednames_fig_1se %in% c(prednames_weath)),
sum(prednames_fig_1se %in% c(prednames_soils))))
row.names(uniqueCoeffs) <- c("RMSE", "bias: mean(obs-pred.)", "Total number of coefficients", "Number of unique coefficients",
"Number of unique climate coefficients",
"Number of unique weather coefficients",
"Number of unique soils coefficients"
)
kable(uniqueCoeffs,
col.names = c("Best lambda model", "1/2 se lambda model", "1se lambda model"), row.names = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) | Best lambda model | 1/2 se lambda model | 1se lambda model | |
|---|---|---|---|
| RMSE | 13.3219741 | 13.7083797 | 14.0657195 |
| bias: mean(obs-pred.) | 0.0894596 | 0.0714672 | 0.0566447 |
| Total number of coefficients | 89.0000000 | 42.0000000 | 22.0000000 |
| Number of unique coefficients | 24.0000000 | 20.0000000 | 14.0000000 |
| Number of unique climate coefficients | 6.0000000 | 4.0000000 | 4.0000000 |
| Number of unique weather coefficients | 14.0000000 | 13.0000000 | 8.0000000 |
| Number of unique soils coefficients | 4.0000000 | 3.0000000 | 2.0000000 |
Visualizations of Model Predictions and Residuals – using best lambda model
observed vs. predicted values
If the 1se lambda has no terms (is an intercept only model), use the 1/2 se lambda in subsequent figures
if (length(prednames_fig_1se) == 0) {
mod_secondBest <- fit_glm_halfse
name_secondBestMod <- "1/2 SE Model"
prednames_secondBestMod <- prednames_fig_halfse
} else {
mod_secondBest <- fit_glm_se
name_secondBestMod <- "1 SE Model"
prednames_secondBestMod <- prednames_fig_1se
}Predicting on the data
# create prediction for each each model
# (i.e. for each fire proporation variable)
predict_by_response <- function(mod, df) {
df_out <- df
response_name <- paste0(response, "_pred")
preds <- predict(mod, newx= df_out, #s="lambda.min",
type = "response")-2
preds[preds<0] <- 0
preds[preds>100] <- 100
df_out <- df_out %>% cbind(preds)
colnames(df_out)[ncol(df_out)] <- response_name
return(df_out)
}
pred_glm1 <- predict_by_response(fit_glm_bestLambda, X[,2:ncol(X)])
## back-transform the
# add back in true y values
pred_glm1 <- pred_glm1 %>%
cbind(modDat_1_s[,c(response, response_t)])
# add back in lat/long data
pred_glm1 <- pred_glm1 %>%
cbind(modDat_1_s[,c("Long", "Lat", "Year")])
pred_glm1$resid <- pred_glm1[,response] - pred_glm1[,paste0(response, "_pred")]
pred_glm1$extremeResid <- NA
pred_glm1[pred_glm1$resid > 70 | pred_glm1$resid < -70,"extremeResid"] <- 1
# plot(x = pred_glm1[,response],
# y = pred_glm1[,paste0(response, "_pred")],
# xlab = "observed values", ylab = "predicted values")
# points(x = pred_glm1[!is.na(pred_glm1$extremeResid), response],
# y = pred_glm1[!is.na(pred_glm1$extremeResid), paste0(response, "_pred")],
# col = "red")pred_glm1_1se <- predict_by_response(mod_secondBest, X[,2:ncol(X)])
# add back in true y values
pred_glm1_1se <- pred_glm1_1se %>%
cbind(modDat_1_s[,c(response, response_t)])
# add back in lat/long data
pred_glm1_1se <- pred_glm1_1se %>%
cbind(modDat_1_s[,c("Long", "Lat", "Year")])
pred_glm1_1se$resid <- pred_glm1_1se[,response] - pred_glm1_1se[,paste0(response, "_pred")]
pred_glm1_1se$extremeResid <- NA
pred_glm1_1se[pred_glm1_1se$resid > 70 | pred_glm1_1se$resid < -70,"extremeResid"] <- 1Maps of Observations, Predictions, and Residuals=
Observations across the temporal range of the dataset
pred_glm1 <- pred_glm1 %>%
mutate(resid = .[[response]] - .[[paste0(response,"_pred")]])
# rasterize
# get reference raster
test_rast <- rast("../../../Data_raw/dayMet/rawMonthlyData/orders/70e0da02b9d2d6e8faa8c97d211f3546/Daymet_Monthly_V4R1/data/daymet_v4_prcp_monttl_na_1980.tif") %>%
terra::aggregate(fact = 8, fun = "mean")## |---------|---------|---------|---------|========================================= ## add ecoregion boundaries (for our ecoregion level model)
regions <- sf::st_read(dsn = "../../../Data_raw/Level2Ecoregions/", layer = "NA_CEC_Eco_Level2") ## Reading layer `NA_CEC_Eco_Level2' from data source
## `/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/PED_vegClimModels/Data_raw/Level2Ecoregions'
## using driver `ESRI Shapefile'
## Simple feature collection with 2261 features and 8 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -4334052 ymin: -3313739 xmax: 3324076 ymax: 4267265
## Projected CRS: Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Arearegions <- regions %>%
st_transform(crs = st_crs(test_rast)) %>%
st_make_valid() #%>%
#st_crop(st_bbox(test_rast))
#
# goodRegions_temp <- st_overlaps(y = cropped_states, x = regions, sparse = FALSE) %>%
# rowSums()
# goodRegions <- regions[goodRegions_temp != 0,]
ecoregionLU <- data.frame("NA_L1NAME" = sort(unique(regions$NA_L1NAME)),
"newRegion" = c(NA, "Forest", "dryShrubGrass",
"dryShrubGrass", "Forest", "dryShrubGrass",
"dryShrubGrass", "Forest", "Forest",
"dryShrubGrass", "Forest", "Forest",
"Forest", "Forest", "dryShrubGrass",
NA
))
goodRegions <- regions %>%
left_join(ecoregionLU)
mapRegions <- goodRegions %>%
filter(!is.na(newRegion)) %>%
group_by(newRegion) %>%
summarise(geometry = sf::st_union(geometry)) %>%
ungroup() %>%
st_simplify(dTolerance = 1000)
#mapview(mapRegions)
# rasterize data
plotObs <- pred_glm1 %>%
drop_na(paste(response)) %>%
#slice_sample(n = 5e4) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast)) %>%
terra::rasterize(y = test_rast,
field = response,
fun = mean) #%>%
#terra::aggregate(fact = 2, fun = mean, na.rm = TRUE) %>%
#terra::crop(ext(-1950000, 1000000, -1800000, 1000000))
# get the extent of this particular raster, and crop it accordingly
tempExt <- crds(plotObs, na.rm = TRUE)
plotObs_2 <- plotObs %>%
crop(ext(min(tempExt[,1]), max(tempExt[,1]),
min(tempExt[,2]), max(tempExt[,2]))
)
# make figures
map_obs <- ggplot() +
geom_spatraster(data = plotObs_2) +
geom_sf(data=cropped_states %>% st_transform(crs = st_crs(test_rast)) %>% st_crop(st_bbox(plotObs_2)),fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
labs(title = paste0("Observations of ", response, " in the ",ecoregion, " ecoregion")) +
scale_fill_gradient2(low = "brown",
mid = "wheat" ,
high = "darkgreen" ,
midpoint = 0, na.value = "grey20") +
xlim(st_bbox(plotObs_2)[c(1,3)]) +
ylim(st_bbox(plotObs_2)[c(2,4)])
hist_obs <- ggplot(pred_glm1) +
geom_histogram(aes(.data[[response]]), fill = "lightgrey", col = "darkgrey")
library(ggpubr)
ggarrange(map_obs, hist_obs, heights = c(3,1), ncol = 1)
Predictions across the temporal range of the dataset
# rasterize data
plotPred <- pred_glm1 %>%
drop_na(paste0(response,"_pred")) %>%
#slice_sample(n = 5e4) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast)) %>%
terra::rasterize(y = test_rast,
field = paste0(response,"_pred"),
fun = mean) #%>%
#terra::aggregate(fact = 2, fun = mean, na.rm = TRUE) %>%
#terra::crop(ext(-1950000, 1000000, -1800000, 1000000))
# get the point location of those predictions that are > 100
highPred_points <- pred_glm1 %>%
filter(.[[paste0(response,"_pred")]] > 100 |
.[[paste0(response, "_pred")]] < 0) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast))
# get the extent of this particular raster, and crop it accordingly
tempExt <- crds(plotPred, na.rm = TRUE)
plotPred_2 <- plotPred %>%
crop(ext(min(tempExt[,1]), max(tempExt[,1]),
min(tempExt[,2]), max(tempExt[,2]))
)
# make figures
map_preds1 <- ggplot() +
geom_spatraster(data = plotPred_2) +
geom_sf(data=cropped_states %>% st_transform(crs = st_crs(test_rast)) %>% st_crop(st_bbox(plotObs_2)),fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
geom_sf(data = highPred_points, col = "red") +
labs(title = paste0("Predictions from the 'best lambda' fitted model of ", response, " in the ",ecoregion, " ecoregion"),
subtitle = "bestLambda model") +
scale_fill_gradient2(low = "wheat",
mid = "darkgreen",
high = "red" ,
midpoint = 100, na.value = "grey20",
limits = c(0,100)) +
xlim(st_bbox(plotObs_2)[c(1,3)]) +
ylim(st_bbox(plotObs_2)[c(2,4)])
hist_preds1 <- ggplot(pred_glm1) +
geom_histogram(aes(.data[[paste0(response, "_pred")]]), fill = "lightgrey", col = "darkgrey")+
xlim(c(0,100))
ggarrange(map_preds1, hist_preds1, heights = c(3,1), ncol = 1)
# rasterize data
plotPred <- pred_glm1_1se %>%
drop_na(paste0(response,"_pred")) %>%
#slice_sample(n = 5e4) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast)) %>%
terra::rasterize(y = test_rast,
field = paste0(response,"_pred"),
fun = mean) #%>%
#terra::aggregate(fact = 2, fun = mean, na.rm = TRUE) %>%
#terra::crop(ext(-1950000, 1000000, -1800000, 1000000))
# get the point location of those predictions that are > 100
highPred_points <- pred_glm1_1se %>%
filter(.[[paste0(response,"_pred")]] > 100 |
.[[paste0(response, "_pred")]] < 0) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast))
# get the extent of this particular raster, and crop it accordingly
tempExt <- crds(plotPred, na.rm = TRUE)
plotPred_2 <- plotPred %>%
crop(ext(min(tempExt[,1]), max(tempExt[,1]),
min(tempExt[,2]), max(tempExt[,2]))
)
# make figures
map_preds2 <- ggplot() +
geom_spatraster(data = plotPred_2) +
geom_sf(data=cropped_states %>% st_transform(crs = st_crs(test_rast)) %>% st_crop(st_bbox(plotObs_2)),fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
geom_sf(data = highPred_points, col = "red") +
labs(title = paste0("Predictions from the '1SE lambda' fitted model of ", response, " in the ",ecoregion, " ecoregion"),
subtitle = name_secondBestMod) +
scale_fill_gradient2(low = "wheat",
mid = "darkgreen",
high = "red" ,
midpoint = 100, na.value = "grey20",
limits = c(0,100)) +
xlim(st_bbox(plotObs_2)[c(1,3)]) +
ylim(st_bbox(plotObs_2)[c(2,4)])
hist_preds2 <- ggplot(pred_glm1_1se) +
geom_histogram(aes(.data[[paste0(response, "_pred")]]), fill = "lightgrey", col = "darkgrey")+
xlim(c(0,100))
ggarrange(map_preds2, hist_preds2, heights = c(3,1), ncol = 1)
Residuals across the entire temporal extent of the dataset
# rasterize data
plotResid_rast <- pred_glm1 %>%
drop_na(resid) %>%
#slice_sample(n = 5e4) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast)) %>%
terra::rasterize(y = test_rast,
field = "resid",
fun = mean) #%>%
#terra::aggregate(fact = 2, fun = mean, na.rm = TRUE) %>%
#terra::crop(ext(-1950000, 1000000, -1800000, 1000000))
# get the extent of this particular raster, and crop it accordingly
tempExt <- crds(plotResid_rast, na.rm = TRUE)
plotResid_rast_2 <- plotResid_rast %>%
crop(ext(min(tempExt[,1]), max(tempExt[,1]),
min(tempExt[,2]), max(tempExt[,2]))
)
# identify locations where residuals are >100 or < -100
badResids_high <- pred_glm1 %>%
filter(resid > 100) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast))
badResids_low <- pred_glm1 %>%
filter(resid < -100) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast))
# make figures
map <- ggplot() +
geom_spatraster(data =plotResid_rast_2) +
geom_sf(data=cropped_states %>% st_transform(crs = st_crs(test_rast)) %>% st_crop(st_bbox(plotObs_2)),fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
geom_sf(data = badResids_high, col = "blue") +
geom_sf(data = badResids_low, col = "red") +
labs(title = paste0("Resids. (obs. - pred.) from the ", ecoregion," ecoregion-wide model of ", response),
subtitle = "bestLambda model \n red points indicate locations that have residuals below -100 \n blue points indicate locatiosn that have residuals above 100") +
scale_fill_gradient2(low = "red",
mid = "white" ,
high = "blue" ,
midpoint = 0, na.value = "grey20",
limits = c(-100,100)
) +
xlim(st_bbox(plotObs_2)[c(1,3)]) +
ylim(st_bbox(plotObs_2)[c(2,4)])
hist <- ggplot(pred_glm1) +
geom_histogram(aes(resid), fill = "lightgrey", col = "darkgrey") +
geom_text(aes(x = min(resid)*.9, y = 1500, label = paste0("min = ", round(min(resid),2)))) +
geom_text(aes(x = max(resid)*.9, y = 1500, label = paste0("max = ", round(max(resid),2)))) +
geom_vline(aes(xintercept = mean(resid)))
ggarrange(map, hist, heights = c(3,1), ncol = 1)
# rasterize data
plotResid_rast <- pred_glm1_1se %>%
drop_na(resid) %>%
#slice_sample(n = 5e4) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast)) %>%
terra::rasterize(y = test_rast,
field = "resid",
fun = mean) #%>%
#terra::aggregate(fact = 2, fun = mean, na.rm = TRUE) %>%
#terra::crop(ext(-1950000, 1000000, -1800000, 1000000))
# get the extent of this particular raster, and crop it accordingly
tempExt <- crds(plotResid_rast, na.rm = TRUE)
plotResid_rast_2 <- plotResid_rast %>%
crop(ext(min(tempExt[,1]), max(tempExt[,1]),
min(tempExt[,2]), max(tempExt[,2]))
)
# identify locations where residuals are >100 or < -100
badResids_high <- pred_glm1_1se %>%
filter(resid > 100) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast))
badResids_low <- pred_glm1_1se %>%
filter(resid < -100) %>%
terra::vect(geom = c("Long", "Lat")) %>%
terra::set.crs(crs(test_rast))
# make figures
map <- ggplot() +
geom_spatraster(data =plotResid_rast_2) +
geom_sf(data=cropped_states %>% st_transform(crs = st_crs(test_rast)) %>% st_crop(st_bbox(plotObs_2)),fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
geom_sf(data = badResids_high, col = "blue") +
geom_sf(data = badResids_low, col = "red") +
labs(title = paste0("Resids. (obs. - pred.) from the ", ecoregion," ecoregion-wide model of ", response),
subtitle = paste0(name_secondBestMod,"\n red points indicate locations that have residuals below -100 \n blue points indicate locatiosn that have residuals above 100")) +
scale_fill_gradient2(low = "red",
mid = "white" ,
high = "blue" ,
midpoint = 0, na.value = "grey20",
limits = c(-100,100)
) +
xlim(st_bbox(plotObs_2)[c(1,3)]) +
ylim(st_bbox(plotObs_2)[c(2,4)])
hist <- ggplot(pred_glm1_1se) +
geom_histogram(aes(resid), fill = "lightgrey", col = "darkgrey") +
geom_text(aes(x = min(resid)*.9, y = 1500, label = paste0("min = ", round(min(resid),2)))) +
geom_text(aes(x = max(resid)*.9, y = 1500, label = paste0("max = ", round(max(resid),2))))+
geom_vline(aes(xintercept = mean(resid)))
ggarrange(map, hist, heights = c(3,1), ncol = 1)
Are there biases of the model predictions across year/lat/long?
# plot residuals against Year
yearResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = jitter(Year), y = resid), alpha = .1) +
geom_smooth(aes(x = Year, y = resid)) +
xlab("Year") +
ylab("Residuals") +
ggtitle("from best lamba model")
yearResidMod_1seLambda <- ggplot(pred_glm1_1se) +
geom_point(aes(x = jitter(Year), y = resid), alpha = .1) +
geom_smooth(aes(x = Year, y = resid)) +
xlab("Year") +
ylab("Residuals") +
ggtitle(paste0("from ", name_secondBestMod))
# plot residuals against Lat
latResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = Lat, y = resid), alpha = .1) +
geom_smooth(aes(x = Lat, y = resid)) +
xlab("Latitude") +
ylab("Residuals") +
ggtitle("from best lamba model")
latResidMod_1seLambda <- ggplot(pred_glm1_1se) +
geom_point(aes(x = Lat, y = resid), alpha = .1) +
geom_smooth(aes(x = Lat, y = resid)) +
xlab("Latitude") +
ylab("Residuals") +
ggtitle(paste0("from ", name_secondBestMod))
# plot residuals against Long
longResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = Long, y = resid), alpha = .1) +
geom_smooth(aes(x = Long, y = resid)) +
xlab("Longitude") +
ylab("Residuals") +
ggtitle("from best lamba model")
longResidMod_1seLambda <- ggplot(pred_glm1_1se) +
geom_point(aes(x = Long, y = resid), alpha = .1) +
geom_smooth(aes(x = Long, y = resid)) +
xlab("Longitude") +
ylab("Residuals") +
ggtitle(paste0("from ", name_secondBestMod))
library(patchwork)
(yearResidMod_bestLambda + yearResidMod_1seLambda) /
( latResidMod_bestLambda + latResidMod_1seLambda) /
( longResidMod_bestLambda + longResidMod_1seLambda)
Quantile plots
Binning predictor variables into “Deciles” (actually percentiles) and looking at the mean predicted probability for each percentile. The use of the word Decentiles is just a legacy thing (they started out being actual Percentiles)
# get deciles for best lambda model
if (length(prednames_fig) == 0) {
print("The best lambda model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
pred_glm1_deciles <- predvars2deciles(pred_glm1,
response_vars = response_vars,
pred_vars = prednames_fig,
cut_points = seq(0, 1, 0.005))
}
# get deciles for 1 SE lambda model
if (length(prednames_secondBestMod) == 0) {
print("The 1SE (or 1/2 SE) lambda model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
pred_glm1_deciles_1se <- predvars2deciles(pred_glm1_1se,
response_vars = response_vars,
pred_vars = prednames_secondBestMod,
cut_points = seq(0, 1, 0.005))
}Here is a quick version of LOESS curves fit to raw data (to double-check the quantile plot calculations)
if (length(prednames_fig) == 0) {
print("The model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
pred_glm1 %>%
dplyr::select(all_of(c(prednames_fig, response_vars))) %>%
pivot_longer(cols = prednames_fig) %>%
ggplot() +
facet_wrap(~name, scales = "free") +
geom_point(aes(x = value, y = .data[[paste(response)]]), col = "darkblue", alpha = .1) + # observed values
geom_point(aes(x = value, y = .data[[response_vars[2]]]), col = "lightblue", alpha = .1) + # model-predicted values
geom_smooth(aes(x = value, y = .data[[paste(response)]]), col = "black", se = FALSE) +
geom_smooth(aes(x = value, y = .data[[response_vars[2]]]), col = "brown", se = FALSE) +
ggtitle(label = "dark blue points = observations;
light blue points = predictions;
black line = observations;
brown line = predictions")
}
Below are the actual quantile plots (note that the predictor variables are scaled)
if (length(prednames_fig) == 0) {
print("The model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
# publication quality version
g3 <- decile_dotplot_pq(pred_glm1_deciles, response = response, IQR = TRUE
#CI = TRUE
) + ggtitle("Decile Plot")
g4 <- add_dotplot_inset(g3, df = pred_glm1_deciles, dfRaw = pred_glm1, add_smooth = TRUE, deciles = FALSE)
if(save_figs) {
png(paste0("figures/quantile_plots/quantile_plot_", response, "_",ecoregion,".png"),
units = "in", res = 600, width = 5.5, height = 3.5 )
print(g4)
dev.off()
}
g4
}
if (length(prednames_secondBestMod) == 0) {
print("The 1 se lambda model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
# publication quality version
g3 <- decile_dotplot_pq(pred_glm1_deciles_1se, response = response, IQR = TRUE) + ggtitle("Decile Plot")
g4 <- add_dotplot_inset(g3, df = pred_glm1_deciles_1se, dfRaw = pred_glm1_1se, add_smooth = TRUE, deciles = FALSE)
if(save_figs) {
png(paste0("figures/quantile_plots/quantile_plot_", response, "_",ecoregion,".png"),
units = "in", res = 600, width = 5.5, height = 3.5 )
print(g4)
dev.off()
}
g4
}
Deciles Filtered
20th and 80th percentiles for each climate variable
df <- pred_glm1[, prednames_fig] #%>%
#mutate(MAT = MAT - 273.15) # k to c
quantiles <- map(df, quantile, probs = c(0.2, 0.8), na.rm = TRUE)Filtered ‘Decile’ plots of data. These plots show each vegetation variable, but only based on data that falls into the upper and lower two deciles of each predictor variable.
if (length(prednames_fig) == 0) {
print("The model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
pred_glm1_deciles_filt <- predvars2deciles( pred_glm1,
response_vars = response_vars,
pred_vars = prednames_fig,
filter_var = TRUE,
filter_vars = prednames_fig,
cut_points = seq(0, 1, 0.005))
decile_dotplot_filtered_pq(pred_glm1_deciles_filt, xvars = prednames_fig)
#decile_dotplot_filtered_pq(pred_glm1_deciles_filt)
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